Picking the low fruit Tony Grift University of Illinois Agenda What do Agricultural Engineers really work on? Top Ten List of journal papers Focus on modeling Example of suspicious modeling Example of essential modeling DEM: Discrete Element Modeling or Madness? What do you do if it get really complicated? How do we judge papers? Are papers like poetry? Can we judge them objectively? If we want objective judgment, can we make a score card? Can we automate the process? What makes a good journal? Nature refuses 63/64 papers.. Does this indicate quality? From: Dead Poets Society, Touchstone Pictures How about these criteria for starters? Can we use these to categorize papers? What do Agricultural Engineers really do? Title & Keywords in ASABE Transactions /Biosystems Electronic Database What do Agricultural Engineers really do? Title & Keywords in Biosystems Engineering Electronic Database (1) Modeling madness papers Dr. Brij Moudgil: member of the National Academy of Engineering and Director of the Particle Engineering Research Center of the University of Florida: The emphasis on modeling in particle engineering research has not helped us; instead it has hurt us Why has there been so much emphasis on modeling in research? That was not a rhetorical question! (2) Observation only papers We observe some relationship and perhaps state a hypothesis to explain the observed phenomenon It is apparently obvious that trying to understand what we see is either too complicated, too costly or should conveniently be left for further research In Results and Discussion.The trenches we dug have presented two clear working hypotheses for explaining the effect of iron soil distribution on the success of magnetic surveys at identifying clay agricultural drainage pipe locations. This is a result? If we have hypotheses, how about testing them? (3) Fit and forget papers Data is collected and a curve is fitted. Even though there is no natural phenomenon out there that adheres to exp(exp(x)), it is proposed as a statistical model Shouldnt we be interpreting our data and use it to form an understanding? Or is the target a high R^2? Is a line fitted through some data points a model? A common mistake is a physical interpretation of intercepts (4) Proof of concept papers An algorithm or device is developed in the lab and the concept is proven by taking PVC tubes for plants, a halogen light to represent the sun and a tennis ball for an orange Have you ever seen a paper where a proof of concept is actually brought to application in the field? Why do people avoid bringing their research to the field? It takes time, money, and why do it, if you can get your paper published without? (5) Test papers With the emergence of (D)GPS we not only had a major stream of papers about its application and accuracy, we decided to devote complete sessions at our annual conferences to it Do we need over a hundred papers to study it? Isnt it the manufacturers obligation to assess the accuracy of their instruments under varying conditions? In 2009, the European GPS system Galileo is projected to come online. Should we already reserve space for another 127 papers about its accuracy and the implication for farmers? (6) Tool time papers There are many applications particularly in Agriculture that justify machine vision approaches However, since cameras are ubiquitous and inexpensive, we have pretended that they are the solution for almost any problem! (7) Simulation papers Mathematical modeling (although the term is inaccurate here) has become simpler, especially with modern tools such as Fluid Dynamics (FLUENT), Finite Element (ANSYS) and Discrete Element software (EDEM) There is a chance that they will spawn many papers that revolve around the tool rather than the problem Simulation tools only mimic reality, they do not explain anything A physiologically correct model of a cat is useful to visualize organs, muscles and tissues but It does not explain why it likes to throw up on your carpet! (8) Grievance papers Everyone complains about the weather but nobody does anything about it (Mark Twain) There are many papers in which an agricultural machine is purchased and tested, usually without endorsing it, and criticized In contrast to GPS receivers, we could actually suggest changes in design to improve the performance, but few do so (9) Safety in numbers papers One buys a machine and writes 25 short papers looking at the machine from many different perspectives under varying conditions How can any of these papers can have rigor and depth? (10) Nothing lost, nothing gained papers Papers that describe the development of indices such as the NDVI and siblings. What do we actually learn from these? Since we do not fundamentally understand the underlying mechanism that causes the NDVI or the Excessive Green trick to work, we can keep on developing indices for decades without being refuted Has anyone ever tried to investigate what the optimal vegetation index is based on solid biochemistry? Wouldnt we be able to use this knowledge for application in other areas such as perhaps the detection of skin cancer? Languages : 14 Countries: 115 Circulation: 1,500,000 Impact factor: 0 Example of suspicious modeling Discrete Element Model of a Granular Fertilizer Spreader Dr. Bert Tijskens, 2007 Dynamic model of spherical particle accelerating along a straight radial vane (Inns and Reece, 1962) Model (ignore gravity) dynamic friction coefficient Measurement of dynamic friction coefficient (dfc) Use of rheometer Controlled force (0.5 1 N) Angular velocity 10 rad/s Continuous force measurement Calculate dfc assuming Coulomb friction model Ammonium Nitrate and Potassium Chloride were used for experiments Ammonium Nitrate NH 4 NO 3 Potassium Chloride KCl Kalman Filter style lumped parameter fitting Predicted quantity Measured quantity Spinning tube device Inserting a particle into the central feed orifice with a suction hose (300 RPM) Dynamic friction coefficient Calcium Ammonium Nitrate (CAN) Dynamic friction coefficient Potassium Chloride (KCL) How does one compare the DEM simulation output with the reality? Do we play until it looks real? Then what? A compaction layer in the soil can be detected by recording the sound of a cone being drawn trough it. Time (s) Do we understand the sound generating mechanism of the cone interacting with the soil? No. Does this mean this research is useless? Correlation of acoustic data and cone penetrometer data Images can be used to observe a maize root and fractal dimension can be used to quantify its complexity. Roots look very similar to simulated fractals (L-systems). Does this mean we can use L-systems to understand roots? Interpretation of fractal dimension to identify Quantitative Trait Loci (QTL) responsible for root complexity Example of essential modeling Mass flow measurement of granular materials How do you count particles when they clump together? Upper Middle Lower The flow can be split into two independent processes, clump and spacing times A single-layer photo interruption device is used to measure the clump and spacing times Assume that the flow forms a random arrival (Poisson) process: The flow density (in particle/second) is equal to the reciprocal value of the mean spacing times This means we can count the number of particles by knowing nothing about them. We only need the times among the clumps! Assuming a Poisson process, the nr. of particles per time unit (flow rate) can be estimated using the following formula Mean of spacing times Mean of clump times Nr of clumps Assuming a Poisson process, the mean particle diameter can also be estimated using the following formula Mean of spacing lengths Mean of clump lengths Simulated and measured clump lengths Simulated and measured spacing lengths Dr. Bert Tijskens, 2007 Is this Discrete Element Model a good representation of reality? To model or not to model Model is essential for mass flow measurement Model developed in concert with measurements Model eliminated need for calibration Because of the model we understand the underlying process Counterintuitive method found (using spacings between particles to count particles) Fundamental concept can be extrapolated to other applications To predict the need for bypass surgery, one needs to know the nr of blood cells in arteries Sensor 5 m MEMS Laser grids can be made with Vertical Cavity Surface Emitting Lasers (VCSEL) 10 m Daisy chained laser grid Development of a mass flow sensor for citrus fruits Sensor What kinds of papers are we losing and does it matter? Bad results papers (not the ones with fabricated data) Truly multidisciplinary papers Repeated research Fundamental papers Complete papers including measurement, simulation, modeling and validation Are we still scientific? Do we care? Sir Karl Poppers definition of a scientific theory Empirical falsifiability as the criterion for distinguishing scientific theory from non-science Scientific method is a body of techniques for investigating phenomena and acquiring new knowledge, as well as for correcting and integrating previous knowledge. It is based on gathering observable, empirical, measurable evidence, subject to the principles of reasoning Do we still follow this method in Ag&B Engineering? Where is the word modeling? Is this the new research paradigm? Get a big grant Buy an expensive instrument, put it in a frame Play with it, get some data Focus on the efficient and convenient Modeling, algorithms, computer programming Squeeze the maximum number of papers out of it Are we still Engineers? How many papers develop a new instrument to gather observable, empirical, measurable evidence? Why do we refrain from developing our own instruments? Why are we losing more and more building support? Grifts recipe for Science Soup If you can, build an instrument Experiment Use your data to understand the problem Use modeling as a tool to support your experimental results or to better understand what you see. Dont regard it as a result Dont confuse the tool with the problem Dig deep, do something that is hard Do something original, exciting and important Carve out your niche, something you want to be known for Focus on your weaknesses, not your strengths Get as scientific as you can Focus on quality, not quantity The aim is to generate new knowledge to further our discipline Go for the high hanging fruit Assignment Comment on the paper you brought in light of the categories in this presentation (1) Modeling madness papers (2) Observation only papers (3) Fit and forget papers (4) Proof of concept papers (5) Test papers (6) Tool time papers (7) Simulation papers (8) Grievance papers (9) Safety in numbers papers (10) Nothing lost, nothing gained papers Thank you!!